Skip to main content

EDITORIAL article

Front. Neuroergonomics , 07 March 2025

Sec. Neurotechnology and Systems Neuroergonomics

Volume 6 - 2025 | https://doi.org/10.3389/fnrgo.2025.1568619

This article is part of the Research Topic Advances in Mobile Optical Brain Activity Monitoring View all 9 articles

Editorial: Advances in mobile optical brain activity monitoring

  • 1Clinical Neurotechnology Lab, Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité – Universitätsmedizin Berlin, Berlin, Germany
  • 2Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
  • 3Institute of Complementary and Integrative Medicine, University of Bern, Bern, Switzerland
  • 4Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
  • 5Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
  • 6Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom
  • 7Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
  • 8Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
  • 9BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
  • 10Technical University Berlin, Intelligent Biomedical Sensing Lab, Berlin, Germany

Advancements in brain imaging have significantly enhanced our understanding of brain function, but much of this progress stems from constrained, single-snapshot experiments conducted in controlled laboratory settings. Understanding brain activity in dynamic, complex, and multisensory real-world environments remains in its infancy. Emerging mobile brain imaging technologies beyond electrocencephalography (EEG) (Nann et al., 2019), such as functional near-infrared spectroscopy (fNIRS) (Boas et al., 2014) or diffuse optical tomography (DOT) (Chitnis et al., 2016), are beginning to bridge this gap, enabling continuous measurement of cerebrovascular activity linked to brain activity induced by, for example, human movement, perception, cognition, social communication, and interaction in naturalistic settings. For instance, portable fNIRS devices have proven effective for monitoring mental workload (Herff et al., 2013; Park, 2023) and can provide real-time feedback, e.g., in the context of brain-computer interface (BCI) applications (Soekadar et al., 2021). In education, fNIRS has been used to study attention (Harrivel et al., 2013), engagement (Verdiere et al., 2018), and learning outcomes (Lamb et al., 2022) in natural settings, while its role in infant development research has expanded understanding of perception and cognition in diverse populations (Gervain et al., 2023). Moreover, hyperscanning (Hakim et al., 2023; Scholkmann et al., 2013) enables simultaneous measurement of brain activity in multiple individuals, revealing mechanisms like inter-brain synchrony during social interactions. Integrating fNIRS with multimodal tools such as EEG (von Luhmann et al., 2017), eye-tracking (Isbilir et al., 2019), and systemic physiological monitoring (Scholkmann et al., 2022) enhances these insights, specifically into learning processes and interpersonal dynamics, paving the way toward new medical and non-medical applications.

The Research Topic “Advances in Mobile Optical Brain Activity Monitoring” underlines the transformative potential of portable fNIRS and related optical techniques for investigating brain function in real-world and dynamic settings. Featuring eight contributions from leading laboratories, this Research Topic highlights cutting-edge advancements in the field.

In her review, Klein underscores the critical importance of spatial specificity and signal quality in real-time fNIRS applications, which are vital for reliable data collection in neurofeedback and BCI contexts. Challenges such as anatomical variability, variations in cap placement, and contamination by extracerebral noise and motion artifacts are addressed, advocating for advanced preprocessing techniques and adaptive algorithms to improve reproducibility and reliability.

Biswas et al. present a novel low-cost approach to non-invasive blood flow monitoring with integrated Diffuse Speckle Contrast Spectroscopy (iDSCS). By leveraging a low-cost photodiode and a custom electronic circuit, iDSCS simplifies deep tissue blood flow measurements, offering a compact, power-efficient alternative to traditional Diffuse Correlation Spectroscopy (DCS). Their study demonstrates the feasibility of wearable probe-level hemodynamic blood flow monitoring.

Bonnaire et al. propose an innovative approach utilizing hyperscanning with fNIRS to study social bond formation in children. The study integrates multimodal data, including conversational behaviors, interpersonal rapport, collaborative tasks, and inter-brain synchrony. The findings aim to deepen understanding of social connectivity while informing the design of empathetic AI systems and personalized educational tools that adapt to group dynamics.

Moffat et al. advocate for mobile fNIRS in longitudinal and intergenerational hyperscanning studies to uncover the neural mechanisms underlying social dynamics across generations. By addressing ecological validity challenges with portable designs and real-world protocols, this perspective article specifically highlights interventions targeting intergenerational relationships, particularly in psychological and social contexts.

Roumengous et al. introduce the NIRSense Aerie, a wearable fNIRS system optimized for high-G environments encountered by military aircrew. The system monitors cerebrovascular oxygenation and perfusion during high-G-force exposure, offering real-time feedback to improve anti-G straining maneuver training and operational safety. Future advancements in miniaturization and comfort will extend its applications to other high-stress occupations.

Lingelbach et al. investigate workload-dependent hemispheric asymmetries in emotion-cognition interactions using an ecologically valid fNIRS setup. Their findings reveal lateralized prefrontal cortex activity influenced by emotional distractions and workload levels, with implications for optimizing focus and productivity in learning and work environments.

Srinivasan et al. demonstrate the importance of incorporating subject-specific information to enhance spatial accuracy in high-density diffuse optical tomography (HD-DOT) using fNIRS. By employing photogrammetry to identify optode placement, their study shows the extend of optode localization errors, particularly in motor cortex recordings that ranged at 27.4 mm in average. Their work underlines the importance of collecting subject-specific optode locations for all wearable NIRS experiments to achieve accurate results.

Finally, von Au et al. examine the neural activation patterns associated with different self-touch behaviors, identifying distinct hemodynamic responses in the prefrontal cortex during phasic and repetitive self-touch. This study shows that repetitive self-touch activates the orbitofrontal cortex and dorsolateral prefrontal cortex brain regions associated with self-regulation, more strongly than phasic self-touch, highlighting the stronger self-regulatory function of repetitive self-touch, the importance of using objective behavioral controls, and the need for future research on irregular self-touch in real-world environments.

Besides underlining the critical challenges that must be addressed to drive the field forward, this Research Topic illustrates the transformative potential of mobile optical brain imaging technologies. While it is essential to advance innovation in instrumentation, data analysis, and experimental design, future efforts must also prioritize interdisciplinary collaborations to fully realize the promise of this exciting new research domain. Importantly, online monitoring of functional brain activity enables dynamic, brain-state-dependent interaction using sensory or brain stimulation (Nasr et al., 2022) or human-computer interaction (neuroadaptive technology, passive BCI) (Zander and Kothe, 2011), offering opportunities to enhance learning, cognition, wellbeing or ergonomics. Establishing neurotech hubs and innovation ecosystems centered around robust academic-industry-clinical collaborations will be crucial for facilitating the rapid prototyping and exploration of such innovative solutions. In this context, it is imperative to ensure that neurotechnology serves as an enabler of human potential and development, rather than a tool for surveillance, coercion, or any application that undermines human freedom or rights (UNESCO, 2024). Embedding these advancements within a robust neuroethical framework is essential to safeguard their responsible use (Garden et al., 2019). With these efforts, mobile optical brain imaging offers a bright future, redefining how we work, learn, and interact with digital technologies.

Author contributions

SS: Conceptualization, Funding acquisition, Project administration, Resources, Writing – original draft, Writing – review & editing. FS: Conceptualization, Project administration, Writing – review & editing. MY: Writing – review & editing. PP: Writing – review & editing. JN: Writing – review & editing. AL: Resources, Supervision, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. SS was supported in part by the European Research Council (ERC) under the project NGBMI (759370), TIMS (101081905) and BNCI2 (101088715), the Deutsche Forschungsgemeinschaft (DFG SO932/7-1), the Federal Ministry of Research and Education (ERA NET Neuron 01GP2121B, CHANSE Q-SHIFT 01UX2211, QHMI 03ZU2110FC), and the Einstein Stiftung Berlin (ESB). AL gratefully acknowledges funding from the German Federal Ministry of Education and Research (Grant No. BIFOLD25B). PP was supported by the Wellcome Trust (212979/Z/18/Z).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Boas, D. A., Elwell, C. E., Ferrari, M., and Taga, G. (2014). Twenty years of functional near-infrared spectroscopy: introduction for the special issue. Neuroimage 85(Pt 1), 1–5. doi: 10.1016/j.neuroimage.2013.11.033

PubMed Abstract | Crossref Full Text | Google Scholar

Chitnis, D., Cooper, R. J., Dempsey, L., Powell, S., Quaggia, S., Highton, D., et al. (2016). Functional imaging of the human brain using a modular, fibre-less, high-density diffuse optical tomography system. Biomed. Opt. Express 7, 4275–4288. doi: 10.1364/BOE.7.004275

PubMed Abstract | Crossref Full Text | Google Scholar

Garden, H., Winickoff, D. E., Frahm, N. M., and Pfotenhauer, S. (2019). “Responsible innovation in neurotechnology enterprises,” in OECD Science, Technology and Industry Working Papers, No. 2019/05 (Paris: OECD Publishing). doi: 10.1787/9685e4fd-en

PubMed Abstract | Crossref Full Text | Google Scholar

Gervain, J., Minagawa, Y., Emberson, L., and Lloyd-Fox, S. (2023). Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges. Neurophotonics 10:023519. doi: 10.1117/1.NPh.10.2.023519

PubMed Abstract | Crossref Full Text | Google Scholar

Hakim, U., De Felice, S., Pinti, P., Zhang, X., Noah, J. A., Ono, Y., et al. (2023). Quantification of inter-brain coupling: a review of current methods used in haemodynamic and electrophysiological hyperscanning studies. Neuroimage 280:120354. doi: 10.1016/j.neuroimage.2023.120354

PubMed Abstract | Crossref Full Text | Google Scholar

Harrivel, A. R., Weissman, D. H., Noll, D. C., and Peltier, S. J. (2013). Monitoring attentional state with fNIRS. Front. Hum. Neurosci. 7:861. doi: 10.3389/fnhum.2013.00861

PubMed Abstract | Crossref Full Text | Google Scholar

Herff, C., Heger, D., Fortmann, O., Hennrich, J., Putze, F., and Schultz, T. (2013). Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS. Front. Hum. Neurosci. 7:935. doi: 10.3389/fnhum.2013.00935

PubMed Abstract | Crossref Full Text | Google Scholar

Isbilir, E., Cakir, M. P., Acarturk, C., and Tekerek, A. S. (2019). Towards a multimodal model of cognitive workload through synchronous optical brain imaging and eye tracking measures. Front. Hum. Neurosci 13:375. doi: 10.3389/fnhum.2019.00375

PubMed Abstract | Crossref Full Text | Google Scholar

Lamb, R., Neumann, K., and Linder, K. A. (2022). Real-time prediction of science student learning outcomes using machine learning classification of hemodynamics during virtual reality and online learning sessions. Comput. Educ. Artif. Intell. 3:100078. doi: 10.1016/j.caeai.2022.100078

Crossref Full Text | Google Scholar

Nann, M., Cohen, L. G., Deecke, L., and Soekadar, S. R. (2019). To jump or not to jump - The Bereitschaftspotential required to jump into 192-meter abyss. Sci. Rep. 9:2243. doi: 10.1038/s41598-018-38447-w

PubMed Abstract | Crossref Full Text | Google Scholar

Nasr, K., Haslacher, D., Dayan, E., Censor, N., Cohen, L. G., and Soekadar, S. R. (2022). Breaking the boundaries of interacting with the human brain using adaptive closed-loop stimulation. Prog. Neurobiol. 216:102311. doi: 10.1016/j.pneurobio.2022.102311

PubMed Abstract | Crossref Full Text | Google Scholar

Park, J. H. (2023). Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity. BMC Neurol. 23:442. doi: 10.1186/s12883-023-03504-z

PubMed Abstract | Crossref Full Text | Google Scholar

Scholkmann, F., Holper, L., Wolf, U., and Wolf, M. (2013). A new methodical approach in neuroscience: assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning. Front. Hum. Neurosci. 7:813. doi: 10.3389/fnhum.2013.00813

PubMed Abstract | Crossref Full Text | Google Scholar

Scholkmann, F., Tachtsidis, I., Wolf, M., and Wolf, U. (2022). Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain. Neurophotonics 9:030801. doi: 10.1117/1.NPh.9.3.030801

PubMed Abstract | Crossref Full Text | Google Scholar

Soekadar, S. R., Kohl, S. H., Mihara, M., and von Lühmann, A. (2021). Optical brain imaging and its application to neurofeedback. Neuroimage Clin. 30:102577. doi: 10.1016/j.nicl.2021.102577

PubMed Abstract | Crossref Full Text | Google Scholar

UNESCO (2024). First draft of the Recommendation on the Ethics of Neurotechnology [Report](SHS/BIO/AHEG-Neuro/2024/2). Paris: UNESCO. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000391444 (accessed January 30, 2025).

PubMed Abstract | Google Scholar

Verdiere, K. J., Roy, R. N., and Dehais, F. (2018). Detecting pilot's engagement using fNIRS connectivity features in an automated vs. manual landing scenario. Front. Hum. Neurosci. 12:6. doi: 10.3389/fnhum.2018.00006

PubMed Abstract | Crossref Full Text | Google Scholar

von Luhmann, A., Wabnitz, H., Sander, T., and Muller, K. R. (2017). M3BA: a mobile, modular, multimodal biosignal acquisition architecture for miniaturized EEG-NIRS-based hybrid BCI and monitoring. IEEE Trans. Biomed. Eng. 64, 1199–1210. doi: 10.1109/TBME.2016.2594127

PubMed Abstract | Crossref Full Text | Google Scholar

Zander, T. O., and Kothe, C. (2011). Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. J. Neural. Eng. 8:025005. doi: 10.1088/1741-2560/8/2/025005

PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: mobile fNIRS, optical brain imaging, wearable neurotechnology, hyperscanning, neuroadaptive technology, real-world brain imaging, neuroethics

Citation: Soekadar SR, Scholkmann F, Yücel MA, Pinti P, Noah JA and von Lühmann A (2025) Editorial: Advances in mobile optical brain activity monitoring. Front. Neuroergonomics 6:1568619. doi: 10.3389/fnrgo.2025.1568619

Received: 30 January 2025; Accepted: 24 February 2025;
Published: 07 March 2025.

Edited and reviewed by: Stephen Fairclough, Liverpool John Moores University, United Kingdom

Copyright © 2025 Soekadar, Scholkmann, Yücel, Pinti, Noah and von Lühmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Surjo R. Soekadar, c3Vyam8uc29la2FkYXJAY2hhcml0ZS5kZQ==

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Research integrity at Frontiers

Man ultramarathon runner in the mountains he trains at sunset

94% of researchers rate our articles as excellent or good

Learn more about the work of our research integrity team to safeguard the quality of each article we publish.


Find out more